Evidence map›Paper›PMID 42898743›Full record

ArticleHealth science reports2026

Advancing Early Breast Cancer Diagnosis and Ki-67 Expression Prediction With Ultrasound Viscoelastography: Based on Peritumoral Assessment.

Xiaoli Huang, Bing Zhang, Fei Chen, Chunxiang Wei, Bei Lan, Huihui Yang, Demao Deng, Quanqing Zou

Abstract read
In one paragraph

Article in Health science reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xiaoli HuangDepartment of Ultrasound The First Affiliated Hospital of Jinan University Guangzhou China.ORCID https://orcid.org/0000-0002-3195-8508
Bing ZhangDepartment of Ultrasound The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences Nanning China.
Fei ChenDepartment of Ultrasound The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences Nanning China.
Chunxiang WeiDepartment of Ultrasound The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences Nanning China.ORCID https://orcid.org/0009-0001-7089-7293
Bei LanDepartment of Ultrasound The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences Nanning China.
Huihui YangDepartment of Ultrasound The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences Nanning China.
Demao DengDepartment of Radiology The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences Nanning China.ORCID https://orcid.org/0000-0002-7565-2607
Quanqing ZouDepartment of Breast Surgery The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences Nanning China.ORCID https://orcid.org/0000-0002-6195-2924

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Breast cancer is the most common invasive malignancy in women. Ultrasound viscoelastography may improve non-invasive diagnosis, but the clinical relevance of peritumoral viscoelastic parameters (VPs) and their correlation with Ki-67 remain unclear. This study explores these associations. Methods: A prospective study enrolled 256 breast masses (156 malignant, 100 benign). Patients underwent conventional ultrasound and viscoelastography. VPs were acquired within masses (A), peritumoral shells (Shell1: 1 mm, Shell2: 2 mm, Shell3: 3 mm), and combined regions (A' = A + Shell). Quantitative metrics included elasticity (V/1 E, V/0 E), dispersion coefficient (V/1 D), viscosity coefficient (V/0 V), and their maximum (Max), minimum (Min), mean(Mean), and standard deviation (SD) values. Diagnostic performance was evaluated by analyzing the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC), followed by logistic regression. VPs were compared between high/low Ki-67 groups. Results: The AUCs of peritumoral VPs exceeded those from masses. V/1 D showed higher AUC than V/0 V and elasticity values, with Shell3 V/1 D SD having the highest AUC (0.840). The diagnostic efficacy of the peritumoral viscoelastic model surpassed that of the single elasticity parameter model, with 89.1% sensitivity, 73% specificity, 82.8% accuracy. Key variables included Shell3 V/1 D and Shell3 V/0 E. Shell1 V/0 V Max was significantly higher in high versus low Ki-67 expression groups ( Conclusion: The viscoelastic model improves diagnostic accuracy by quantifying peritumoral biomechanics, and certain VPs may predict Ki-67 expression. Peritumoral VPs show potential as novel non-invasive imaging biomarkers for early breast cancer evaluation.

Indexed as

breast cancerdispersionKi‐67peritumoralultrasoundviscoelasticviscoelastography

Identifiers

PMID42898743
PMCPMC13678117

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.